[2024] Valid DEA-C01 test answers & Snowflake DEA-C01 exam pdf
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NEW QUESTION # 13
Mark the Correct Statements:
Statement 1. Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database.
Statement 2. Data Engineer can use zero-copy cloning feature for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).
- A. Statement 1
- B. Statement 2
- C. Both are False.
- D. Statement 1 & 2 are correct.
Answer: C
Explanation:
Explanation
Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database and create a derived copy of that object which initially shares the underlying storage. This can be extremely useful for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).
For example, when a clone is created of a table, the clone utilizes no data storage because it shares all the existing micro-partitions of the original table at the time it was cloned; however, rows can then be added, deleted, or updated in the clone independently from the original table. Each change to the clone results in new micro-partitions that are owned exclusively by the clone and are protect-ed through CDP.
NEW QUESTION # 14
To help manage STAGE storage costs, Data engineer recommended to monitor stage files and re-move them from the stages once the data has been loaded and the files which are no longer needed. Which option he can choose to remove these files either during data loading or afterwards?
- A. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
- B. Files no longer needed, can be removed using the PURGE=TRUE command.
- C. Files no longer needed, can be removed using the REMOVE command.
- D. Script can be used during data loading & post data loading with DELETE command.
Answer: A,B
Explanation:
Explanation
Managing Data Files
Staged files can be deleted from a Snowflake stage (user stage, table stage, or named stage) using the following methods:
Files that were loaded successfully can be deleted from the stage during a load by specifying the PURGE copy option in the COPY INTO <table> command.
After the load completes, use the REMOVE command to remove the files in the stage.
Removing files ensures they aren't inadvertently loaded again. It also improves load performance, because it reduces the number of files that COPY commands must scan to verify whether existing files in a stage were loaded already.
NEW QUESTION # 15
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?
- A. FALSE
- B. TRUE
Answer: B
Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.
NEW QUESTION # 16
Select the incorrect statements regarding Clustering depth?
- A. A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 1.
(Correct) - B. It helps Monitoring the clustering "health" of a large table, particularly over time as DML is performed on the table.
- C. The clustering depth for a populated table measures the average depth (1 or greater) of the overlapping micro-partitions for specified columns in a table. The smaller the aver-age depth, the better clustered the table is with regards to the specified columns.
- D. Clustering depth can be used for determining whether a large table would benefit from explicitly defining a clustering key.
Answer: A
Explanation:
Explanation
A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 0.
NEW QUESTION # 17
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?
- A. Spark Connector
- B. Node.js connector
- C. Python Connector
- D. Kafka Connector
Answer: D
Explanation:
Explanation
Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a pub-lisher can queue a message in Kafka for the subscriber to receive later.
An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.
Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).
The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:
RECORD_CONTENT. This contains the Kafka message.
RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.
NEW QUESTION # 18
Steven created the task, what additional privileges required by Steven on the task so that he can suspend or resume the tasks?
- A. Steven must have SUSPEND privilege on the task so that he can suspend or resume the task.
- B. Steven is already owner of the task; he can execute the task & suspend/resume the task without any additional privileges.
- C. In addition to the task owner, a Steven Role must have OPERATE privilege on the task so that he can suspend or resume the task.
- D. Steven needs to have Global Managed RESUME privilege by TASK administrator.
Answer: C
Explanation:
Explanation
In addition to the task ownership privilege, a role that has the OPERATE privilege on the task can suspend or resume the task.
NEW QUESTION # 19
A Data Engineer wants to centralize grant management to maximize security. A user needs ownership on a table m a new schema However, this user should not have the ability to make grant decisions What is the correct way to do this?
- A. Add the with managed access parameter on the schema
- B. Grant ownership to the user on the table
- C. Revoke grant decisions from the user on the schema.
- D. Revoke grant decisions from the user on the table
Answer: A
Explanation:
Explanation
The with managed access parameter on the schema enables the schema owner to control the grant and revoke privileges on the objects within the schema. This way, the user who owns the table cannot make grant decisions, but only the schema owner can. This is the best way to centralize grant management and maximize security.
NEW QUESTION # 20
Which stages support external tables?
- A. Internal stages only; within a single Snowflake account
- B. External stages only from any region, and any cloud provider
- C. External stages only, only on the same region and cloud provider as the Snowflake account
- D. internal stages only from any Snowflake account in the organization
Answer: B
Explanation:
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.
NEW QUESTION # 21
A Data Engineer needs to know the details regarding the micro-partition layout for a table named invoice using a built-in function.
Which query will provide this information?
- A. SELECT $CLUSTERXNG_INFQRMATION ('Invoice')'
- B. CALL SYSTEM$CLUSTERING_INFORMATION ('Invoice');
- C. CALL $CLUSTERINS_INFORMATION('Invoice');
- D. SELECT SYSTEM$CLUSTERING_INTFORMATICII ('Invoice' ) ;
Answer: D
Explanation:
Explanation
The query that will provide information about the micro-partition layout for a table named invoice using a built-in function is SELECT SYSTEM$CLUSTERING_INFORMATION('Invoice');. The SYSTEM$CLUSTERING_INFORMATION function returns information about the clustering status of a table, such as the clustering key, the clustering depth, the clustering ratio, the partition count, etc. The function takes one argument: the table name in a qualified or unqualified form. In this case, the table name is Invoice and it is unqualified, which means that it will use the current database and schema as the context. The other options are incorrect because they do not use a valid built-in function for providing information about the micro-partition layout for a table. Option B is incorrect because it uses $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which is not a valid function name. Option C is incorrect because it uses CALL instead of SELECT, which is not a valid way to invoke a table function.
Option D is incorrect because it uses CALL instead of SELECT and $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which are both invalid.
NEW QUESTION # 22
Which are the Cloud Platforms that Support Calling an External Function?
- A. AWS & AZURE
- B. AWS,GCP,AZURE
- C. GCP
- D. AWS only
Answer: B
NEW QUESTION # 23
Which system role is recommended for a custom role hierarchy to be ultimately assigned to?
- A. SECURITYADMIN
- B. USERADMIN
- C. SYSTEMADMIN
- D. ACCOUNTADMIN
Answer: A
Explanation:
Explanation
The system role that is recommended for a custom role hierarchy to be ultimately assigned to is SECURITYADMIN. This role has the manage grants privilege on all objects in an account, which allows it to grant access privileges to other roles or revoke them as needed. This role can also create or modify custom roles and assign them to users or other roles. By assigning custom roles to SECURITYADMIN, the role hierarchy can be managed centrally and securely. The other options are not recommended system roles for a custom role hierarchy to be ultimately assigned to. Option A is incorrect because ACCOUNTADMIN is the most powerful role in an account, which has full access to all objects and operations. Assigning custom roles to ACCOUNTADMIN can pose a security risk and should be avoided. Option C is incorrect because SYSTEMADMIN is a role that has full access to all objects in the public schema of the account, but not to other schemas or databases. Assigning custom roles to SYSTEMADMIN can limit the scope and flexibility of the role hierarchy. Option D is incorrect because USERADMIN is a role that can manage users and roles in an account, but not grant access privileges to other objects. Assigning custom roles to USERADMIN can prevent the role hierarchy from controlling access to data and resources.
NEW QUESTION # 24
Which property can be used with ALTER USER command to temporarily disable MFA for the user so that they can log in?
- A. HOURS_TO_BYPASS_MFA
- B. MINS_TO_BYPASS_MFA
- C. MINS_TO_SKIP_MFA
- D. SECS_TO_BYPASS_MFA
Answer: B
Explanation:
Explanation
You can use the following properties for the ALTER USER command to perform these tasks:
MINS_TO_BYPASS_MFA
Specifies the number of minutes to temporarily disable MFA for the user so that they can log in. Af-ter the time passes, MFA is enforced and the user cannot log in without the temporary token gener-ated by the Duo Mobile application.
NEW QUESTION # 25
Alex, a Data Engineer with one of the Data analytics Organization, created the Materialized view over External tables to improve Data Reporting Experience.
Step 1: He created materialized view named DataReportMV
1.create or replace materialized view DataReportMV as
2.select Item_id, Item_price from Items;
Step 2: He joined a materialized view with a sales table as
1.create or replace view Revenue as
2.select m.item_id, sum(ifnull(s.quantity, 0)) as quantity,
3.sum(ifnull(quantity * (s.price - m.item_price), 0)) as profit
4.from DataReportMV as m left outer join sales as s on s.item_id = m.item_id
5.group by m.item_id;
Step 3: After 1 hour, he decided to temporarily suspend the use (and maintenance) of the DataRe-portMV materialized view for cost saving purpose.
alter materialized view DataReportMV suspend;
Please select what Alex is doing wrong here?
- A. There is no command like suspend for temporarily suspension of Materialized views, Step 3 will give error like invalid Suspend command.
- B. A materialized view, DataReportMV does not support Join operations, so Step 2 would be failed & he cannot proceed further.
- C. Once DataReportMV got suspended , any query on the top of the view will generate er-ror like:
Failure during expansion of view 'DATAREPORTMV': SQL compilation error: Material-ized view DataReportMV is invalid. - D. Alex is doing everything correct.
- E. Materialized view on top of External tables is not supported feature.
Answer: D
Explanation:
Explanation
All Steps will be executed successfully by Alex without any error.
NEW QUESTION # 26
A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data engineer needs to move these transformations to Snowpark.
...characteristics of data transformations in Snowpark should be considered to meet this requirement? (Select TWO)
- A. Columns in different DataFrames with the same name should be referred to with squared brackets
- B. It is possible to join multiple tables using DataFrames.
- C. Snowpark requires a separate cluster outside of Snowflake for computations
- D. Snowpark operations are executed lazily on the server.
- E. User-Defined Functions (UDFs) are not pushed down to Snowflake
Answer: B,D
Explanation:
Explanation
The characteristics of data transformations in Snowpark that should be considered to meet this requirement are:
It is possible to join multiple tables using DataFrames.
Snowpark operations are executed lazily on the server.
These characteristics indicate how Snowpark can perform data transformations using DataFrames, which are similar to the ones used in Scala. DataFrames are distributed collections of rows that can be manipulated using various operations, such as joins, filters, aggregations, etc. DataFrames can be created from different sources, such as tables, files, or SQL queries. Snowpark operations are executed lazily on the server, which means that they are not performed until an action is triggered, such as a write or a collect operation. This allows Snowpark to optimize the execution plan and reduce the amount of data transferred between the client and the server.
The other options are not characteristics of data transformations in Snowpark that should be considered to meet this requirement. Option C is incorrect because User-Defined Functions (UDFs) are pushed down to Snowflake and executed on the server. Option D is incorrect because Snowpark does not require a separate cluster outside of Snowflake for computations, but rather uses virtual warehouses within Snowflake. Option E is incorrect because columns in different DataFrames with the same name should be referred to with dot notation, not squared brackets.
NEW QUESTION # 27
Data Engineer looking out for quick tool for understanding the mechanics of queries & need to know more about the performance or behaviour of a particular query.
He should go to which feature of snowflake which can help him to spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities?
- A. Query Profile
- B. Query Designer
- C. Performance Metadata table
- D. Query Optimizer
Answer: A
Explanation:
Explanation
Query Profile, available through the classic web interface, provides execution details for a query. For the selected query, it provides a graphical representation of the main components of the pro-cessing plan for the query, with statistics for each component, along with details and statistics for the overall query.
Query Profile is a powerful tool for understanding the mechanics of queries. It can be used whenev-er you want or need to know more about the performance or behavior of a particular query. It is de-signed to help you spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities.
NEW QUESTION # 28
Ron, Snowflake Developer needs to capture change data (insert only) on the source views, for that he follows the below steps:
Enable change tracking on the source views & its underlying tables.
Inserted the data via Scripts scheduled with the help of Tasks.
then simply run the below Select statements.
1.select *
2.from test_table
3.changes(information => append_only)
4.at(timestamp => (select current_timestamp()));
Select the Correct Query Execution Output option below:
- A. Select statement complied but gives erroneous results.
- B. Developer missed to create stream on the source table which can further query to cap-ture DML records.
- C. Select query will fail with error: 'SQL compilation error-Incorrect Keyword "Chang-es()" found'
- D. No Error reported, select command gives Changed records with Metadata columns as change tracking enabled on the Source views & its underlying tables.
Answer: D
Explanation:
Explanation
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.
To Know more about Snowflake CHANGES clause, please refer the mentioned link:
https://docs.snowflake.com/en/sql-reference/constructs/changes
NEW QUESTION # 29
Which Role that is dedicated to user and role management only?
- A. PUBLIC
- B. ORGADMIN
- C. USERADMIN
- D. SECURITYADMIN
- E. SYSADMIN
Answer: C
NEW QUESTION # 30
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?
- A. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
- B. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
- C. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
- D. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
Answer: A
Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.
NEW QUESTION # 31
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?
- A. FALSE
- B. TRUE
(Correct)
Answer: B
NEW QUESTION # 32
A Data Engineer is evaluating the performance of a query in a development environment.
Based on the Query Profile what are some performance tuning options the Engineer can use? (Select TWO)
- A. Move the query to a larger virtual warehouse
- B. Create indexes to ensure sorted access to data
- C. Increase the max cluster count
- D. Use a multi-cluster virtual warehouse with the scaling policy set to standard
- E. Add a LIMIT to the ORDER BY If possible
Answer: A,E
Explanation:
Explanation
The performance tuning options that the Engineer can use based on the Query Profile are:
Add a LIMIT to the ORDER BY If possible: This option will improve performance by reducing the amount of data that needs to be sorted and returned by the query. The ORDER BY clause requires sorting all rows in the input before returning them, which can be expensive and time-consuming. By adding a LIMIT clause, the query can return only a subset of rows that satisfy the order criteria, which can reduce sorting time and network transfer time.
Create indexes to ensure sorted access to data: This option will improve performance by reducing the amount of data that needs to be scanned and filtered by the query. The query contains several predicates on different columns, such as o_orderdate, o_orderpriority, l_shipmode, etc. By creating indexes on these columns, the query can leverage sorted access to data and prune unnecessary micro-partitions or rows that do not match the predicates. This can reduce IO time and processing time.
The other options are not optimal because:
Use a multi-cluster virtual warehouse with the scaling policy set to standard: This option will not improve performance, as the query is already using a multi-cluster virtual warehouse with the scaling policy set to standard. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on the load. Changing the warehouse size or the number of clusters will not affect the performance of this query, as it is already using the optimal resources.
Increase the max cluster count: This option will not improve performance, as the query is not limited by the max cluster count. The max cluster count is a parameter that specifies the maximum number of clusters that a multi-cluster virtual warehouse can scale up to. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on theload. The default max cluster count for a 2XL warehouse is 10, which means that the warehouse can scale up to 10 clusters if needed. However, the query does not need more than 4 clusters, as it is not CPU-bound or memory-bound. Increasing the max cluster count will not affect the performance of this query, as it will not use more clusters than necessary.
NEW QUESTION # 33
The COPY command supports several options for loading data files from a stage i.e.
I. By path
II. Specifying a list of specific files to load.
III. Using pattern matching to identify specific files by pattern.
IV. Organize files into logical paths that reflect a scheduling pattern.
Of the aforesaid options for identifying/specifying data files to load from a stage, which option in general is the fastest & best considerate?
- A. I
- B. III
- C. IV
- D. II
Answer: D
Explanation:
Explanation
Of the above options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
For example:
copy into load1 from @%load1/Snow1/ files=('mydata1.csv', 'mydata2.csv', 'mydata3.csv')
NEW QUESTION # 34
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